mirror of
https://github.com/pewdiepie-archdaemon/odysseus.git
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RaresKeYandStressTestor
7026cf40b5
* docs(specs): restore bootstrap after dev rewrite
* docs(specs): remove runtime inventory snapshot
* docs(specs): reconcile current dev truth
* docs(specs): document scheduled task actions as an owner-attribution source
Owner Attribution covered cookie, bearer-token and internal-loopback
requests. Scheduled task actions are a fourth source and behave
differently: _execute_action passes owner=task.owner off the stored
ScheduledTask row, so no request and no resolved principal are in
flight, and route-level require_user() never runs.
Webhook triggers are the sharp case. They are unauthenticated by
design with the token as the only credential and execute under the
stored task.owner.
Paths cite routes/task/task_routes.py, the canonical location after
the task subpackage move (#6081); routes/task_routes.py on current dev
is the backward-compat shim.
* docs(specs): add chained tasks to the trigger list, refresh dev stamp
Review feedback from RaresKeY on the previous commit.
"Every trigger path" was too broad: success-chained tasks are another
path into _execute_action. Added them with their own citation, and
noted that chaining additionally requires the target task to share
task.owner and rejects cycles, which is stricter than the trigger-side
checks. Softened the lead-in to "these trigger paths".
Line 56 still pointed at routes/task_routes.py for webhook credential
validation. That path is the backward-compat shim on current dev after
the task subpackage move (#6081); repointed to the canonical
routes/task/task_routes.py.
Stamp moved to dev@2a6b09b. Inspection backing that bump was scoped:
every file path cited in this spec was mechanically checked to resolve
on 2a6b09b, and every file:line in the Owner Attribution additions was
read against it. Behavioral claims elsewhere in the file were not
re-audited.
* docs(specs): correct SECURE_COOKIES description to match current behavior
Third of the stale details RaresKeY enumerated. The cookie section
described SECURE_COOKIES as purely opt-in, which stopped being true.
_secure_cookie() (routes/auth_routes.py:89) treats an explicit true or
false as authoritative and derives the Secure attribute from the
request otherwise, including when the variable is unset and when
docker-compose injects it present-but-empty. Either the connection
scheme or the first X-Forwarded-Proto hop being https is enough.
* docs(specs): refresh current dev truth
---------
Co-authored-by: StressTestor <212606152+StressTestor@users.noreply.github.com>
35 lines
1.6 KiB
Markdown
35 lines
1.6 KiB
Markdown
# OpenAI Provider Shape
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Last updated: dev@e71f8ce | 2026-08-25
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## Scope
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Canonical provider ID `openai`; API dialects OpenAI Chat Completions and
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Responses; catalog reader `src/model_capability_readers/openai.py`.
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## Catalog Shape
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`GET /v1/models` returns `object: list` with `data[]` model cards containing
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`id`, `object`, `created`, and `owned_by`. This is identity and availability
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metadata only. It does not claim vision, tools, reasoning, modality, task, or
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context length. The record remains unknown and keeps the raw fields.
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## Request And Response Shape
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Chat uses `messages`, `tools[].function`, `tool_choice`, and
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`choices[].message|delta`; Responses uses `input`, flattened tools, output
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items, and typed stream events. OpenAI may support a parameter at the platform
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level while individual models differ. A later model registry or probe must
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scope that fact before it becomes canonical model capability.
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## Fallback And Safety
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An explicit endpoint kind selects this provider. Automatic reader detection accepts exact `openai.com` or a dot-delimited subdomain after normalizing case/trailing dots; it is a normalization hint rather than a trust boundary. Do not parse model IDs or ownership labels. If a proxy returns richer fields while explicitly configured as OpenAI, the reader preserves them as raw evidence but keeps capability unknown.
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## Current Gaps
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- OpenAI's Models API does not publish the per-model capability shape needed
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for automatic canonical classification.
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- Runtime model-specific sampling/reasoning behavior still needs a maintained
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structured registry or endpoint probes.
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